Happiness Project
Andy Lee
1 Project Description
The data was collected from Gallup World Poll. Their survey consisted of questions that asked participants to rank their own life on a Cantril ladder with a scale from 1 to 10, 10 being the best ideal way of living and 0 being the worst. This data set focuses on the happiness score of each country, which ranges from 0 to 10. Each country is ranked based on that averaged happiness score for participants. The team recorded scores for these factors: economy or GDP per Capita, family or social support, health or life expectancy, and freedom to help explain the happiness score of each country.
library(socviz)
library(lubridate)
library(geofacet)
library(ggthemes)
library(ggrepel)
library(ggridges)
library(plyr)
library(skimr)
library(tidyverse)
library(gganimate)
library(plotly)
library(stargazer) # regression tables
library(ggstatsplot)
library(corrr)
theme_set(theme_classic())2 Data Wrangling
2.1 Happiness Data
# Read 2015 Data
h15 <- read_csv("Happiness_Data/2015.csv")
h15 <- h15 %>%
dplyr::mutate(Year = 2015) %>%
dplyr::rename(H_rank=`Happiness Rank`, # Modify variable names
H_score = `Happiness Score`,
GDP=`Economy (GDP per Capita)`,
Health=`Health (Life Expectancy)`,
Trust=`Trust (Government Corruption)`,
SE=`Standard Error`,
dystopia_res = `Dystopia Residual`)
# Read 2016 Data
h16 <- read_csv("Happiness_Data/2016.csv")
h16 <- h16 %>%
dplyr::mutate(Year = 2016,
`Standard Error` = (`Upper Confidence Interval`-`Lower Confidence Interval`)/3.92) %>%
# SE = (upper limit – lower limit) / 3.92.
# This is for 95% CI
dplyr::select(-c(`Upper Confidence Interval`,`Lower Confidence Interval`)) %>%
dplyr::rename(H_rank=`Happiness Rank`, # Modify variable names
H_score = `Happiness Score`,
GDP=`Economy (GDP per Capita)`,
Health=`Health (Life Expectancy)`,
Trust=`Trust (Government Corruption)`,
SE=`Standard Error`,
dystopia_res = `Dystopia Residual`)
# Since we don't have a variable 'Region' starting from 2017, we will create it for
# each year
h_regions <- dplyr::select(h16, Country, Region)
# Read 2017 Data
h17 <- read_csv("Happiness_Data/2017.csv")
h17 <- h17 %>%
dplyr::mutate(Year = 2017,
`Standard Error` = (`Whisker.high`-`Whisker.low`)/3.92,) %>%
merge(h_regions,by="Country", all.x=T) %>%
dplyr::select(-c(`Whisker.high`,`Whisker.low`)) %>%
dplyr::rename(H_rank=`Happiness.Rank`, # Modify variable names
H_score = Happiness.Score,
GDP=Economy..GDP.per.Capita.,
Health=Health..Life.Expectancy.,
Trust=Trust..Government.Corruption.,
SE=`Standard Error`,
dystopia_res = Dystopia.Residual)
# Read 2018 Data
h18 <- read_csv("Happiness_Data/2018.csv")
h18 <- h18 %>%
dplyr::mutate(Year = 2018) %>%
dplyr::rename(H_rank=`Overall rank`, # Modify variable names
H_score = `Score`,
GDP=`GDP per capita`,
Country = `Country or region`,
Health=`Healthy life expectancy`,
Trust=`Perceptions of corruption`,
Freedom = `Freedom to make life choices`,
Family = `Social support`) %>%
merge(h_regions,by="Country", all.x=T) %>%
dplyr::mutate(dystopia_res = H_score - (GDP + Family + Health + Freedom + Generosity + as.numeric(Trust)))
# Read 2019 Data
h19 <- read_csv("Happiness_Data/2019.csv")
h19 <- h19 %>%
dplyr::mutate(Year = 2019) %>%
dplyr::rename(H_rank=`Overall rank`, # Modify variable names
H_score = `Score`,
GDP=`GDP per capita`,
Country = `Country or region`,
Health=`Healthy life expectancy`,
Trust=`Perceptions of corruption`,
Freedom = `Freedom to make life choices`,
Family = `Social support`) %>%
merge(h_regions,by="Country", all.x=T) %>%
dplyr::mutate(dystopia_res = H_score -
(GDP + Family + Health + Freedom + Generosity + as.numeric(Trust)))
# Combine all data into all_dat
h_alldat <- tibble(rbind.fill(h15,h16,h17,h18,h19))
h_alldat <- h_alldat %>%
dplyr::mutate(Country = as.factor(tolower(Country)),
Region = as.factor(Region))
#rmarkdown::paged_table(h_alldat)
save(h_alldat, file = 'h_alldat.RData')
knitr::kable(papeR::summarize_numeric(h_alldat, type = "numeric", group = "Region",variables = c("H_rank"), test = FALSE))| Region | N | Mean | SD | Min | Q1 | Median | Q3 | Max | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | H_rank | Australia and New Zealand | 10 | 9.10 | 1.10 | 8 | 8.0 | 9.0 | 10.0 | 11 | |||
| 1.1 | Central and Eastern Europe | 144 | 75.63 | 26.80 | 20 | 55.5 | 73.0 | 91.5 | 138 | ||||
| 1.2 | Eastern Asia | 28 | 66.32 | 22.54 | 25 | 52.0 | 65.0 | 83.5 | 101 | ||||
| 1.3 | Latin America and Caribbean | 109 | 49.75 | 29.97 | 12 | 28.0 | 43.0 | 63.0 | 148 | ||||
| 1.4 | Middle East and Northern Africa | 96 | 79.56 | 41.40 | 11 | 39.0 | 83.0 | 109.0 | 156 | ||||
| 1.5 | North America | 10 | 11.30 | 5.14 | 5 | 7.0 | 11.0 | 15.0 | 19 | ||||
| 1.6 | Southeastern Asia | 44 | 80.55 | 35.45 | 22 | 46.5 | 81.5 | 107.0 | 145 | ||||
| 1.7 | Southern Asia | 35 | 112.46 | 23.31 | 67 | 97.0 | 115.0 | 127.5 | 154 | ||||
| 1.8 | Sub-Saharan Africa | 185 | 126.86 | 21.41 | 55 | 114.0 | 131.0 | 143.0 | 158 | ||||
| 1.9 | Western Europe | 103 | 26.12 | 26.36 | 1 | 6.0 | 17.0 | 36.0 | 102 |
2.2 MERGE: Death and Risk Factors Data & Happiness Data
# Read data in
death_dat <- read_csv('/Volumes/Programming/Spring 2022/DANL 310/my_website/aLin-96.github.io/Happiness_Data/number-of-deaths-by-risk-factor.csv')
death_dat <- death_dat %>%
filter(Year > 2015) %>%
arrange(Year)
rmarkdown::paged_table(death_dat)2.3 MERGE: Country Profile UN Data & Happiness Data
country_profile <- read_csv('/Volumes/Programming/Spring 2022/DANL 310/my_website/aLin-96.github.io/Happiness_Data/kiva_country_profile_variables.csv')
country_profile <- country_profile %>%
select(-c(`GDP per capita (current US$)`)) %>%
dplyr::mutate(country = tolower(country)) %>%
dplyr::rename(Country = country,
Life_expectancy = `Life expectancy at birth (females/males, years)`,
Urban_pop = `Urban population (% of total population)`,
Phone_subscriptions = `Mobile-cellular subscriptions (per 100 inhabitants)...41`,
Employment_rate = `Employment: Services (% of employed)`,
GVA_services = `Economy: Services and other activity (% of GVA)`,
Infant_mortality = `Infant mortality rate (per 1000 live births`,
Age_distribution = `Population age distribution (0-14 / 60+ years, %)`,
Fertility_rate = `Fertility rate, total (live births per woman)`,
Sanitation_facilities = `Pop. using improved sanitation facilities (urban/rural, %)`,
Urban_pop_growthrate = `Urban population growth rate (average annual %)`,
GVA_agriculture = `Economy: Agriculture (% of GVA)`,
Pop_growthRate = `Population growth rate (average annual %)`,
Energy_production = `Energy production, primary (Petajoules)`
) %>%
separate(Life_expectancy, c('Life_expectancy_F','Life_expectancy_M'), sep = "/") %>%
separate(Age_distribution, c('Age_distribution_below14','Age_distribution_above60'), sep = "/") %>%
dplyr::select(-c(Region)) %>%
mutate(Life_expectancy_F = as.numeric(Life_expectancy_F),
Life_expectancy_M = as.numeric(Life_expectancy_M),
Life_expectancy_F = if_else(Life_expectancy_F < mean(Life_expectancy_F),
"Under Average",
"Above Average"),
Life_expectancy_M = if_else(Life_expectancy_M < mean(Life_expectancy_M),
"Under Average",
"Above Average")) # Change the Life_expectancy variables into categorical variables
h_p_alldat <- merge(h_alldat, country_profile, by = "Country")
rmarkdown::paged_table(country_profile)Find Meaningful Variables related to Happiness Score
Top 10 Positive & Negative Correlation Coefficients
h_p_corr <- data.matrix(h_p_alldat, rownames.force = NA) %>%
correlate() %>%
stretch() %>%
filter(x != y & x == "H_score" &
y != "H_rank" &
y != "Net Official Development Assist. received (% of GNI)") %>%
arrange(desc(r))
# Top 10 Positive Correlation Coefficients
h_p_corr_positive10 <- h_p_corr %>%
head(10)
# Top 10 Negative Correlation Coefficients
h_p_corr_negative10 <- h_p_corr %>%
arrange(r) %>%
head(10)Top 10 Positive Correlation Coefficients
Top 10 Negative Correlation Coefficients
2.4 TOP 10 AVG Hppiness Scores (2015 ~ 2019)
# Get Top 10 mean of happiness rank from 2015 ~ 2019
top_10 <- h_alldat %>%
group_by(Country) %>%
dplyr::summarise(mean_rank = mean(H_rank)) %>%
arrange(desc(mean_rank)) %>%
filter(mean_rank <= 10)
rmarkdown::paged_table(top_10)
3 Visualizations
3.1 Boxplot of Happiness Scores in different Regions
ggplot(dplyr::filter(h_alldat, Region != "NA")) +
geom_boxplot(aes(x = H_score, y=reorder(Region, H_score), color = Region))+
theme_classic() +
theme(legend.position = "None") +
labs(x = "Happiness Scores", y = "Regions")
3.2 Happiness Scores vs GDP
ggplot(dplyr::filter(h_alldat, Region != "NA"), aes(x = GDP, y=H_score, color = Region)) +
geom_point() +
theme_classic()+
labs(title = "Happiness Scores vs GDP by Region\n")
base <- h_alldat %>%
plot_ly(x = ~GDP, y = ~H_score,
text = ~Country, hoverinfo = "text",
width = 800, height = 500, size = 2)
base %>%
add_markers(color = ~Region, frame = ~Year, ids = ~Country) %>%
animation_opts(1000, easing = "elastic", redraw = FALSE) %>%
animation_slider(
currentvalue = list(prefix = "YEAR ", font = list(color="red"))
) 3.3 World Map by Happiness Scores
world_map <- map_data("world")
world <- world_map %>%
dplyr::rename(Country = region) %>%
dplyr::mutate(Country = str_to_lower(Country),
Country = ifelse(
Country == "usa",
"united states", Country),
Country = ifelse(
Country == "democratic republic of the congo",
"congo (kinshasa)", Country),
Country = ifelse(
Country == "republic of congo",
"congo (brazzaville)", Country),
Country = as.factor(Country))
h_alldat_world <- left_join(h_alldat, world, by = "Country",all.x=TRUE)
p <- ggplot(h_alldat_world, aes(long, lat, group = group,
fill = H_score,
frame = Year))+
geom_polygon(na.rm = TRUE)+
scale_fill_gradient(low = "white", high = "#FD8104", na.value = NA) +
theme_map()
p %>%
plotly::ggplotly() %>%
animation_opts(1000, easing = "elastic",transition = 0, redraw = FALSE)